A writer named Steven Ruiz has an article in USA Today worth spending some time on. There is the whole body of analytical data that fronts the article. He then documents some modern wrinkles in how the pass rush is now being coached.

He spends a fair amount of time talking about the zone blitz, something that in my eyes is not new, and to be plain, was used at least as early as the college TCU-SMU games of the 1930s, the Sammy Baugh games where SMU constantly varied the pressure Sammy would see (1). Different linemen would fall out of the lines and occupy zones, and the folks rushing would vary. This story pretty much gets told in any biography of Sammy Baugh. It’s not hidden in the depths of newspapers.com.

By the 1940s, you see lineman falling back into zones in books by coaches such as Dana Bible (2). Don Faurot, in his book on the Split T, says when speaking of the pass rush: “rush at least four men. Vary the number constantly.”(3)

Patterns like these continued into the 1950s, where books by guys like Bobby Layne then talk about the changes in how the rush was generated once Tom Landry’s 4-3 came into the fore.

In the 5-2 Eagle, as Layne explains, the rush largely came from the ends and the three linemen in the middle defended the run. In the 1950s era 4-3 system, all four down linemen rushed. All the linemen were tall men, with long arms to obscure the view of the quarterback. They all rushed because the wall of arms was a big factor in preventing downfield vision. And because they all rushed, and the 4-3 was a massively dominant defense, from the middle 1950s to the middle 1970s, the notion of a variable line rush was slowly lost.

So, in the modern context, Dick LeBeau is considered the father of the ‘zone blitz’, the modern incarnation of the 1940s ‘constantly variable rush’. And further, the faked blitz, is no longer just talked about or seen. It’s not, as a 1930s coach might put it, part of the ‘bag of tricks’ a defensive player should have. The creeper, as it’s called, is a coaching point that’s integral to some defensive systems. The idea is of course, not new, as anyone who ever saw the Jimmy Johnson coached Philadelphia Eagles defenses can attest to. The thing that’s new are that these kinds of ideas are integrated into defensive systems, are coaching points. And let’s give Steven Ruiz a +1 or thumbs up for exposing all that to us.

***

Hat Tip to Doug Farrar for exposing me to Steven’s post on Twitter.

Notes and References.

1. Holley, Chapter 4.

2. Bible, p. 156.

3. Faurot, p 223.

Bibliography.

Bible, Dana X., Championship Football, Prentice-Hall, New York, 1947.

Faurot, Don, “Secrets of the Split T formation”, Prentice-Hall, 1950.

Holley, Joe, Slingin’ Sam: The Life and Times of the Greatest Quarterback Ever to Play the Game, University of Texas Press, 2012 [ebook].

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First things first. You cannot hurt yourselves much by buying Doug Farrar’s new book “The Genius of Desperation”. I have only one complaint about it. It does mangle the history of the one gap 4-3 when it discusses the Miami 4-3 that Jimmy Johnson helped introduce into the pros. From the beginning there were one gap 4-3s. Just, the 4-3s of Tom Landry were about gap control, not hard core pursuit. Otherwise it’s a very good book. Oh yes, the first edition has some issues in the diagrams, but if he gets a second edition, perhaps those will be fixed.

Dr Z’s classic now has a Kindle edition. If you have Kindle Unlimited, you can get the book for free (for now).

Also, for a limited time, Coach Paul Alexander has a video of the back and forth of Super Bowl LIII, of the 5 UP defense the Patriots used, the tricks the Rams used, and how both teams adapted to defeat the respective defenses. Just, its now unlisted
(can’t be searched for) and it may disappear in time. Don’t say I didn’t warn you.

Well, the system went 0-2, and probably would have gone 1-1 had the refs been able to call pass interference in the last two minutes of the Saints-Rams game. The story as I gather it, is the lack of experience in the current crop of referees and the lack of good positioning during the infraction. Count me among the folks who think “the booth” should be able to overrule this kind of blatant miss on the field.

The other factors that don’t seem to change is that New England outplays its strength of schedule and that Kansas City underperforms its playoff predictions. All that said, the formula predicts a close game with the Rams emerging with a victory.

Super Bowl Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Los Angeles Rams New England Patriots 0.380 0.59 2.8

Not much to say, other than my system went 4-0 predicting winners. It did predict a bigger margin of victory in the Kansas City game, and closer games than most would have expected in the Saints game and Rams game. I don’t think in all honesty, that my odds were that much different from Vegas odds.

Once again, my data favor the home team, and by more than HFA. In both cases the home teams faced tougher competition throughout the year than the challenger.

Conference (NFC/AFC) Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
New Orleans Saints LA Rams 0.986 0.73 7.3
Kansas City Chiefs NE Patriots 1.162 0.76 8.6

 
In this instance the old and new formulas are close in terms of their predictions. That is because the strength of schedule adjustments between the teams are a little larger in the old formula.

Conference (NFC/AFC) Playoff Odds Old Formula
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
New Orleans Saints LA Rams 0.89 0.71 6.6
Kansas City Chiefs NE Patriots 1.092 0.75 8.1

In terms of picking winners, the system went 2-2, unable really to deal with tough underdogs such as the Chargers and Colts. It picked Philadelphia, which by traditional means was the most in favor of the home team, though that game was one foot from being a Chicago win. So it went 2-0 in the NFC and 0-2 in the AFC.

In this round the home teams are favored in all four contests, but by varying amounts compared to the spread.

The methodology of how we pick is given here. The 2018 worksheet is given here. And as an aside, Doug Farrar’s new football book is very very good and I recommend that hard core fans buy it.

In the worksheet below, the factor 0.66 is the logit of home field advantage as calculated by the logistic regression. That’s equivalent to a HFA of 4.9 points. The playoff HFA of 62.7% is equivalent to 3.8 points. So, if you prefer 3.8 or even 3, just subtract 1.1 points or 1.9 points from the points margin respectively. Just for yucks we calculated the Rams and Cowboys odds both with the 0.66 factor of the fitted formula and the 0.518 factor of actual results, the latter in parentheses.

Whether I stick with this new formula is up in the air. I have an older formula that is much the same but not inclined to generate 15 point advantages, a bit tamer, if you will. We’ll see. I don’t do this for a living, just for fun, and the methodology link above gives the old formula.

That said, the second round worksheets.

Second Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
New Orleans Saints Philadelphia Eagles 0.685 0.66 5.1
LA Rams Dallas Cowboys 0.48 (0.34) 0.62 (0.58) 3.6 (2.5)
Kansas City Chiefs Indianapolis Colts 2.067 0.89 15
New England Patriots LA Chargers 0.942 0.72 7.0

 
Update: decided to add the old formula predictions, and also use the measured HFA factor.

 

Second Round Playoff Odds Old Formula
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
New Orleans Saints Philadelphia Eagles 0.546 0.63 4.0
LA Rams Dallas Cowboys 0.313 0.58 2.3
Kansas City Chiefs Indianapolis Colts 1.707 0.85 12.6
New England Patriots LA Chargers 0.42 0.60 3.1

It’s a new playoff season, and another time to try our new playoff formulas. Methodology of this work is described in depth here.

The playoff formulas like New Orleans and Kansas City. They like Baltimore, but Baltimore, which will lose home field after the first round, is unlikely to be favored after that point. The formulas place a substantial penalty on the lack of playoff experience, and so does not favor Chicago, the Chargers, or the Colts. Update: Baltimore has not been in the playoff since 2014, and so the results have been amended.

2017 NFL Playoff Teams, C&F Worksheet.
NFC
Rank Name Home Field Adv Playoff Experience SOS Total Score
1 New Orleans Saints 0.660 0.747 0.192 1.599
2 LA Rams 0.660 0.747 -0.134 1.273
3 Chicago Bears 0.660 0.0 -0.711 -0.051
4 Dallas Cowboys 0.660 0.747 0.046 1.453
5 Seattle Seahawks 0.0 0.747 -0.170 0.577
6 Philadelphia Eagles 0.0 0.747 0.167 0.914
AFC
1 Kansas City Chiefs 0.660 0.747 -0.033 1.374
2 New England Patriots 0.660 0.747 -0.535 0.872
3 Houston Texans 0.660 0.747 -0.465 0.942
4 Baltimore Ravens 0.660 0 0.195 0.855
5 LA Chargers 0.0 0.0 -0.070 -0.070
6 Indianapolis Colts 0.0 0.0 -0.693 -0.693

 
The total score of a particular team is used as a base. Subtract the score of the opponent and the result is the logit of the win probability for that game. You can use the inverse logit (see Wolfram Alpha to do this easily) to get the probability, and you can multiply the logit of the win probability by 7.4 to get the estimated point spread.

Because the worksheet above can be hard to decipher, for the first week of the 2018 playoffs, I’ve done all this for you, in the table below. Odds are presented from the home team’s point of view:

First Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Chicago Bears Philadelphia Eagles -0.965 0.276 -7.1
Dallas Cowboys Seattle Seahawks 0.876 0.706 6.5
Houston Texans Indianapolis Colts 1.635 0.836 12.1
Baltimore Ravens LA Chargers 0.925 0.716 6.8

 

But to summarize, the formulas used here were found by logistic regressions and each element in the formula has a playoff significance of 95%. I promise if the more common offense metrics could say that, they would. I’ll also note that in vogue stats like FPI don’t really give answers markedly different from other common offensive metrics, such as Pythagorean expectation.

That said, offensive metrics like Pythagorean Expectation favor Seattle over Dallas by about half a point, or 52% win probability for Seattle. Offensive stats still favor Baltimore, but not as much. Simple Ranking stats favor Chicago by around 8 points, circa 75% WP. Houston-Indianapolis have approximately even offensive stats, so the difference between the teams is about 3 points. HFA is worth a bit more in the playoffs, circa 63%.

Final games of the season! Philly makes it, and Minnesota does not. Time now to crunch some playoff numbers.

Global Statistics:
Games  Home Wins HwPct Winning_Score Losing_Score Margin
256       153     59.8      28.70        17.60     11.09

Calculated Pythagorean Exponent:  2.86


Rank  Team    Median  GP   W   L   T  Pct   Pred   SRS    MOV   SOS
------------------------------------------------------------------------
1     NO       8.0    16  13   3   0  81.2  73.5  10.07   9.44  0.63
2     LA       6.0    16  13   3   0  81.2  71.2   8.49   8.94 -0.44
3     KC       7.0    16  12   4   0  75.0  69.9   8.89   9.00 -0.11
4     CHI      7.0    16  12   4   0  75.0  75.7   6.28   8.62 -2.34
5     LAC      4.0    16  12   4   0  75.0  68.0   5.95   6.19 -0.23
6     NE       9.5    16  11   5   0  68.8  69.9   5.18   6.94 -1.76
7     HOU      3.0    16  11   5   0  68.8  66.6   3.85   5.38 -1.53
8     BAL      5.5    16  10   6   0  62.5  70.5   7.02   6.38  0.64
9     IND      3.0    16  10   6   0  62.5  65.9   3.36   5.56 -2.21
10    SEA      3.0    16  10   6   0  62.5  64.6   4.51   5.06 -0.56
11    DAL      2.5    16  10   6   0  62.5  53.2   1.09   0.94  0.15
12    PIT      3.0    16   9   6   1  59.4  62.1   5.57   4.25  1.32
13    TEN      3.0    16   9   7   0  56.2  51.6   0.23   0.44 -0.21
14    PHI      2.5    16   9   7   0  56.2  53.8   1.73   1.19  0.55
15    MIN      1.0    16   8   7   1  53.1  53.9   0.59   1.19 -0.60
16    CLE     -1.0    16   7   8   1  46.9  43.7  -0.33  -2.06  1.73
17    ATL     -2.0    16   7   9   0  43.8  48.5  -0.11  -0.56  0.45
18    CAR     -2.0    16   7   9   0  43.8  48.9   0.93  -0.38  1.30
19    WAS     -5.0    16   7   9   0  43.8  33.2  -4.95  -4.88 -0.07
20    MIA     -6.5    16   7   9   0  43.8  29.4  -8.83  -7.12 -1.71
21    GB      -2.5    16   6   9   1  40.6  45.6  -2.74  -1.50 -1.24
22    DEN     -2.5    16   6  10   0  37.5  45.8  -0.51  -1.25  0.74
23    DET     -2.5    16   6  10   0  37.5  42.5  -3.01  -2.25 -0.76
24    CIN     -4.0    16   6  10   0  37.5  35.3  -3.41  -5.44  2.02
25    BUF     -5.5    16   6  10   0  37.5  28.0  -6.90  -6.56 -0.34
26    NYG     -2.5    16   5  11   0  31.2  42.2  -2.17  -2.69  0.51
27    TB      -3.0    16   5  11   0  31.2  38.9  -2.56  -4.25  1.69
28    JAX     -3.0    16   5  11   0  31.2  32.6  -4.02  -4.44  0.41
29    SF      -4.5    16   4  12   0  25.0  33.5  -5.54  -5.81  0.28
30    NYJ     -7.0    16   4  12   0  25.0  30.9  -7.84  -6.75 -1.09
31    OAK    -14.0    16   4  12   0  25.0  20.4  -9.31 -11.06  1.75
32    ARI    -11.0    16   3  13   0  18.8  14.0 -11.50 -12.50  1.00